Future seasonal surface temperature predictability with and without ARISE-stratospheric aerosol injection-1.5
In one sentence. Neural networks on ARISE-SAI-1.5 simulations show that stratospheric aerosol injection changes seasonal surface temperature predictability, especially in ENSO teleconnection regions in boreal winter.
Abstract
Abstract To help reduce anthropogenic climate change impacts, various forms of solar radiation modification have been proposed to reduce the rate of warming. One method to intentionally reflect sunlight into space is through the introduction of reflective particles into the stratosphere, known as stratospheric aerosol injection (SAI). Previous research has shown that SAI implementation could lead to future climate impacts beyond surface temperature, including changes in El Niño Southern Oscillation (ENSO) variability. This response has the potential to modulate midlatitude variability and predictability through atmospheric teleconnections. Here, we explore possible differences in seasonal surface temperature predictability under a future with and without SAI implementation, using neural networks and the ARISE-SAI-1.5 simulations. We find significant future predictability changes in both boreal summer and winter under SSP2-4.5 with and without SAI. However, during boreal winter when SAI is implemented, seasonal predictability is more similar to the base climate than when SAI is not implemented, particularly in regions impacted by ENSO teleconnections.
Labels
- Topic
- Sub-seasonal to seasonal, Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Other
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1088/2752-5295/ad9b43 OpenAlex:W4405098554
- Added
- 2026-10-22
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